3 papers
cs.CV2024
Depth on Demand: Streaming Dense Depth from a Low Frame Rate Active Sensor
Andrea Conti, Matteo Poggi, Valerio Cambareri +1
High frame rate and accurate depth estimation plays an important role in several tasks crucial to robotics and automotive perception. To date, this can be achieved through ToF and…
cs.CV2024
LiDAR-Event Stereo Fusion with Hallucinations
Luca Bartolomei, Matteo Poggi, Andrea Conti +1
Event stereo matching is an emerging technique to estimate depth from neuromorphic cameras; however, events are unlikely to trigger in the absence of motion or the presence of larg…
cs.CV2024
Range-Agnostic Multi-View Depth Estimation With Keyframe Selection
Andrea Conti, Matteo Poggi, Valerio Cambareri +1
Methods for 3D reconstruction from posed frames require prior knowledge about the scene metric range, usually to recover matching cues along the epipolar lines and narrow the searc…